Instead of converting or mapping 3D models into other domains, they can be analyzed in the point cloud domain directly.
Due to the impressive results of PointNet [1]}, most studies have been devoted to learning directly in the point cloud domain.
Since PointNet does not consider the local pattern of a given 3D point cloud, PointNet++ [2]} was proposed, which uses a hierarchical application of PointNet to multiple subsets of a 3D point cloud.
Inspired by DenseNet [3]}, DensePoint [4]} was introduced that learns a dense contextual representation for point cloud processing via a deep hierarchy architecture.
Exploitation of other aspects of local structure with PointNet are
also reported in [5]}, [6]}.
Superpoint [7]} partitions the point cloud into geometrical homogeneous elements and then a graph convolution network is applied to such local elements.
The main drawback of these methods is their lack of shape awareness.
More precisely, such methods do not explicitly model the local spatial layout of points. To this end, several works have been developed [8]}, [9]}, [10]}, [11]} that capture the spatial layout of a point cloud by learning a high-level relation expression among 3D points.
